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A recently conducted scientific study named “Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?” offers fascinating findings about the relationship between news and the price of Bitcoin, the cryptocurrency with the largest market value.
Unlike previous similar studies, which largely used trading volume to evaluate the impact of news on price, this study is particularly notable for its use of news impact analysis on the price as a direct indicator.
24,316 News Articles from 64 News Sites Used
The researchers gathered 24,316 news articles about Bitcoin from 64 news sites. To analyze the impact of these news articles on Bitcoin’s price and generate information flow data, they used Natural Language Processing (NLP) and Spearman correlation, branches of the Artificial Intelligence (AI) field that enable computers to comprehend and process human language.
The results of the study reveal that negative news significantly affects the Bitcoin price and supports the Sequential Information Arrival Hypothesis (SIAH). This situation implies that both individual and institutional investors exhibit high sensitivity towards negative news about Bitcoin. The study also indicates that the relationship between positive news and Bitcoin price validates the Mixture of Distributions Hypothesis (MDH), suggesting high sensitivity of both individual and institutional investors towards positive news.
Trading Volume Is Not A Real Indicator
The study also exposes the limitations of using trading volume as an indicator to determine the effect of news on price. Although trading volume has often been used in similar studies in the past, it can be influenced by factors like news-specific information, irrational trading, and liquidity effect, making it difficult to fully capture the impact of news on price. The use of sentiment analysis and NLP techniques enables a more comprehensive and accurate measurement of the impact of news on Bitcoin’s price. The study also highlights the increasing importance of sensitivity analysis to news in various fields and the potential use of advanced machine learning and deep learning models for efficient analysis.
The research focused on Bitcoin, rather than altcoins and other assets, particularly due to its increasing popularity and investor interest during the COVID-19 pandemic and aggressive money printing by central banks. The study, which uses a vast dataset of Bitcoin news compiled from various sources and sentiment analysis to measure the impact of news on price, aids in understanding the relationship between sentiment and price movement by distinguishing between positive and negative news sensitivity.
In summary, the study shows that news is extremely important for the Bitcoin price, with negative news supporting the SIAH and positive news supporting the MDH hypothesis. The use of sentiment analysis and NLP techniques, compared to traditional options like trading volume, allows for a more direct and comprehensive depiction of the impact of news on price.
In addition to providing valuable information to assist investors in making informed decisions about their Bitcoin investments, the study stands out for filling the gap in academic literature regarding the role of news on price.
